There were approximately 00,000 end-stage renal disease (ESRD) patients receiving dialysis in the United States at the end of 2013. ESRD has a substantial impact on mortality, morbidity, health care cost and quality of life. The preferred therapy, kidney transplantation, is in relatively short supply; e.g., less than 15,000 kidney transplants occurred in the U.S. in 2013, with about 99,000 patients remaining on the wait list at year end. Problem: Given that mortality and hospitalization rates are quite high among ESRD patients, flexible, broadly applicable and easily implementable methods of analysis are required for modeling hospitalization, death, and the two processes simultaneously. Existing methods either fail to target quantities of interest in Aims 1-3 (below), or do so using strong assumptions which limit their applicability. Overall Objective: The overarching goal of this project is to deveop survival analysis methodology to support analyses that will produce a deeper understanding of morbidity and mortality patterns among ESRD patients. Such increased understanding should lead to improvements in renal replacement therapy and, in turn, improved survival and quality of life among ESRD patients. Target Audience: With respect to methodology, the target audience includes biostatisticians, particularly practitioners studying ESRD and other chronic illnesses. Results based on the proposed analyses would be of interest to nephrologists, transplant surgeons and ESRD patients. Products: Novel and innovative methods for the analysis of survival and recurrent event data. Specific Aim 1: Recurrent/terminal events with covariate-dependent association Methods for jointly analyzing recurrent (e.g., hospitalization) and terminating (e.g., death) event data will be developed, then applied to the Dialysis Outcomes and Practice Patterns Study (DOPPS). Specific Aim 2: Process regression for hospital-free survival Methods for modeling the probability of survival and being out-of-hospital will be developed and applied to DOPPS data. Dependent censoring is accommodated, and probability patterns over follow-up time need not be estimated. Specific Aim 3: Direct modeling of restricted mean survival time Methods will be developed for directly modeling mean survival time (capped at a pre-specified value). Application will be to pre-transplant mortality among patients wait-listed for kidney transplant, using Scientific Registry of Transplant Recipients (SRTR) data. For each Aim, the methods will be easily implementable since pertinent software will be developed.